Expectation and Attention in Visual Cognition

视觉认知中的期望和注意力

• 批准号: 8656443
• 负责人: Tobias Egner
• 金额: $ 34.47万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-04-29 至 2017-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8656443
• 关键词: Address; Affect; Attention; Attention deficit hyperactivity disorder; Basic Science; Behavioral; Blood flow; Brain; Clinical; Code; Complex; Computer Simulation; Conflict (Psychology); Data; Decision Making; Detection; Diagnostic; Disease; Electroencephalography; Environment; Etiology; Failure; Foundations; Functional Magnetic Resonance Imaging; Goals; Human; Image; Impulse Control Disorders; Impulsivity; Individual Differences; Knowledge; Life; Link; Literature; Magnetism; Measurement; Metric; Modeling; Nature; Noise; Outcome Measure; Patient Self-Report; Patients; Perception; Personal Satisfaction; Personality Traits; Placebo Effect; Play; Population; Probability; Process; Protocols documentation; Psychophysics; Research; Research Design; Resolution; Retinal; Risk Factors; Schizophrenia; Sensory; Sensory Process; Signal Transduction; Source; Staging; Stimulus; Structure; Testing; Time; Twin Multiple Birth; Uncertainty; Vision; Visual; Visual Cortex; Visual Perception; Visual system structure; Work; addiction; area striata; base; clinically relevant; design; encephalography; expectation; extrastriate visual cortex; follow-up; improved; indexing; meetings; neuromechanism; neuropsychiatry; novel; patient population; programs; psychotic symptoms; public health relevance; relating to nervous system; response; visual cognition; visual information; visual process; visual processing

DESCRIPTION (provided by applicant): An accurate interpretation of the visual environment is crucial to our survival. However, visual perception is faced with two major constraints. First, computational limitations of the visual system render it impossible to simultaneously analyze all aspects of our surroundings with high fidelity. To meet this challenge, vision relies on mechanisms of attention, prioritizing the processing of those aspects of the environment deemed most relevant to our wellbeing. Second, visual information is inherently ambiguous, as many distinct objects can cast an identical retinal image, while a single object can cast many different images. The brain meets this challenge by exploiting statistical regularities in the environment to form perceptual expectations, providing context- sensitive guidance regarding the most probable visual data. Though expectation and attention are closely interwoven in everyday life, they have typically either been investigated in isolation or confounded with each other, such that their relation remains poorly understood. We here argue that the disentangling of the twin influences of attention (stimulus relevance) and expectation (stimulus probability) on perception is a key to major advances in our understanding of visual cognition, including the resolution of longstanding debates in the attention literature (does attention act in an additive o a multiplicative fashion? Does attention act early or late?). Moreover, this question is deeply relevant to clinical conditions in which attentive and predictive processes appear to be deficient, such as attention deficit hyperactivity disorder and schizophrenia. The overall goal of this projec is to determine the computational and neural mechanisms of expectation and attention in visual cognition. We break down this goal into three specific aims. Firstly, we will dissect the computational mechanisms by which expectation and attention modulate visual perception, by examining the timing (early vs. late) and nature (additive vs. multiplicative) of their respective influences on signal detection and visual neural responses. Secondly, we will determine whether and how attention and expectation interact in their modulation of visual processing. Finally, we will exploit the computational metrics developed in this work to lay the foundations for translational computational neuropsychiatry applications, by linking individual differences in attention and expectation model parameters in healthy subjects to variance in personality traits that constitute known risk-factors for clinical diseases whose etiology involves deficits in attentive and predictive processing. These aims will be addressed with a combination of computational simulations, psychophysical testing, self- report, functional magnetic resonance imaging (fMRI), electro-, and magneto-encephalography (EEG, MEG). The proposed work bridges traditionally segregated research on attention and expectation, and advances our knowledge of how humans make sense of, and prioritize, their visual environment. It is furthermore directly relevant to improving our understanding of potential 'failure modes' of visual cognition in patient populations who have difficulty with controlling attention or with accurately predicting and interpreting sensory information.

描述（由申请人提供）：对视觉环境的准确解释对于我们的生存至关重要。然而，视觉感知面临两个主要限制。首先，视觉系统的计算限制使得不可能同时高保真地分析我们周围环境的各个方面。为了应对这一挑战，视觉依赖于注意力机制，优先处理被认为与我们的福祉最相关的环境方面。其次，视觉信息本质上是模糊的，因为许多不同的物体可以投射出相同的视网膜图像，而单个物体可以投射出许多不同的图像。大脑通过利用环境中的统计规律形成感知期望，提供有关最可能的视觉数据的上下文敏感指导来应对这一挑战。尽管期望和注意力在日常生活中紧密交织在一起，但它们通常要么被孤立地研究，要么被相互混淆，因此人们对它们之间的关系仍然知之甚少。我们在这里认为，解开注意力（刺激相关性）和期望（刺激概率）对知觉的双重影响是我们理解视觉认知取得重大进展的关键，包括解决注意力文献中长期存在的争论（注意力是否存在）。注意力以加法还是乘法的方式起作用？）。此外，这个问题与临床状况密切相关，在这些情况下，注意力和预测过程似乎存在缺陷， 例如注意力缺陷多动障碍和精神分裂症。该项目的总体目标是确定视觉认知中期望和注意力的计算和神经机制。我们将这个目标分解为三个具体目标。首先，我们将通过检查期望和注意力对信号检测和视觉神经反应各自影响的时间（早期与晚期）和性质（加法与乘法）来剖析期望和注意力调节视觉感知的计算机制。其次，我们将确定注意力和期望在视觉处理的调节中是否以及如何相互作用。最后，我们将利用这项工作中开发的计算指标，通过将健康受试者注意力和期望模型参数的个体差异与构成临床疾病已知危险因素的人格特征差异联系起来，为转化计算神经精神病学应用奠定基础。病因涉及注意力和预测处理的缺陷。这些目标将通过计算模拟、心理物理测试、自我报告、功能磁共振成像（fMRI）、脑电图和脑磁图（EEG、MEG）的结合来实现。拟议的工作弥合了传统上对注意力和期望的分离研究，并增进了我们对人类如何理解和优先考虑其视觉环境的了解。此外，它还与提高我们对视觉潜在“故障模式”的理解直接相关。 难以控制注意力或难以准确预测和解释感官信息的患者群体的认知。